OpticalMark Recognition(OMR)systems have been studied since 1970.It is widely accepted as a data entry technique.OMR technology is used for surveys and multiple-choice questionnaires.Due to its ease of use,OMR technol...OpticalMark Recognition(OMR)systems have been studied since 1970.It is widely accepted as a data entry technique.OMR technology is used for surveys and multiple-choice questionnaires.Due to its ease of use,OMR technology has grown in popularity over the past two decades and is widely used in universities and colleges to automatically grade and grade student responses to questionnaires.The accuracy of OMR systems is very important due to the environment inwhich they are used.TheOMRalgorithm relies on pixel projection or Hough transform to determine the exact answer in the document.These techniques rely on majority voting to approximate a predetermined shape.The performance of these systems depends on precise input from dedicated hardware.Printing and scanning OMR tables introduces artifacts that make table processing error-prone.This observation is a fundamental limitation of traditional pixel projection and Hough transform techniques.Depending on the type of artifact introduced,accuracy is affected differently.We classified the types of errors and their frequency according to the artifacts in the OMR system.As a major contribution,we propose an improved algorithm that fixes errors due to skewness.Our proposal is based on the Hough transform for improving the accuracy of bias correction mechanisms in OMR documents.As a minor contribution,our proposal also improves the accuracy of detecting markers in OMR documents.The results show an improvement in accuracy over existing algorithms in each of the identified problems.This improvement increases confidence in OMR document processing and increases efficiency when using automated OMR document processing.展开更多
考试是教学过程中一个重要的环节，对考试试卷质量及考试成绩及时、有效地分析，是教学管理部门非常重要的工作内容之一。文章从教学管理策划和教学管理软件开发角度，探讨了计算机和OMR（optical mark reader）技术在考试分析中的应用...考试是教学过程中一个重要的环节，对考试试卷质量及考试成绩及时、有效地分析，是教学管理部门非常重要的工作内容之一。文章从教学管理策划和教学管理软件开发角度，探讨了计算机和OMR（optical mark reader）技术在考试分析中的应用和实现，并给出一个具体的自动阅卷和分析系统实例的设计原理和开发过程。展开更多
提出一种基于光学乐谱识别(Optical Music Recognition,OMR)技术的适合于二值乐谱图像中嵌入其相应MIDI(Musical Instrument Digital Interface)信息的盲水印算法,可实现数字乐谱信息和MIDI信息的重建.实验结果表明,此算法不会对乐谱识...提出一种基于光学乐谱识别(Optical Music Recognition,OMR)技术的适合于二值乐谱图像中嵌入其相应MIDI(Musical Instrument Digital Interface)信息的盲水印算法,可实现数字乐谱信息和MIDI信息的重建.实验结果表明,此算法不会对乐谱识别产生任何影响,且有利于数字音乐乐谱和数字音序的完整性审计和同步传播.展开更多
基金King Saud University for funding this work through Researchers Supporting Project number(RSP2022R426).
文摘OpticalMark Recognition(OMR)systems have been studied since 1970.It is widely accepted as a data entry technique.OMR technology is used for surveys and multiple-choice questionnaires.Due to its ease of use,OMR technology has grown in popularity over the past two decades and is widely used in universities and colleges to automatically grade and grade student responses to questionnaires.The accuracy of OMR systems is very important due to the environment inwhich they are used.TheOMRalgorithm relies on pixel projection or Hough transform to determine the exact answer in the document.These techniques rely on majority voting to approximate a predetermined shape.The performance of these systems depends on precise input from dedicated hardware.Printing and scanning OMR tables introduces artifacts that make table processing error-prone.This observation is a fundamental limitation of traditional pixel projection and Hough transform techniques.Depending on the type of artifact introduced,accuracy is affected differently.We classified the types of errors and their frequency according to the artifacts in the OMR system.As a major contribution,we propose an improved algorithm that fixes errors due to skewness.Our proposal is based on the Hough transform for improving the accuracy of bias correction mechanisms in OMR documents.As a minor contribution,our proposal also improves the accuracy of detecting markers in OMR documents.The results show an improvement in accuracy over existing algorithms in each of the identified problems.This improvement increases confidence in OMR document processing and increases efficiency when using automated OMR document processing.
文摘考试是教学过程中一个重要的环节，对考试试卷质量及考试成绩及时、有效地分析，是教学管理部门非常重要的工作内容之一。文章从教学管理策划和教学管理软件开发角度，探讨了计算机和OMR（optical mark reader）技术在考试分析中的应用和实现，并给出一个具体的自动阅卷和分析系统实例的设计原理和开发过程。
文摘提出一种基于光学乐谱识别(Optical Music Recognition,OMR)技术的适合于二值乐谱图像中嵌入其相应MIDI(Musical Instrument Digital Interface)信息的盲水印算法,可实现数字乐谱信息和MIDI信息的重建.实验结果表明,此算法不会对乐谱识别产生任何影响,且有利于数字音乐乐谱和数字音序的完整性审计和同步传播.
